Personalised electronic messages to improve sun protection in young adults

The incidence of all skin cancers, including melanoma, continues to rise. It is well known that ultraviolet (UV) radiation is the main environmental risk factor for skin cancer, and excessive exposure at a young age increases the risk of developing skin cancer. The aim of this study was to determine the acceptability and feasibility of delivering sun protection messages via electronic media such as short message services (SMS) to people 18-40 years, and explore factors associated with their acceptability. Overall, 80% of participants agreed that they would like to receive some form of sun protection advice; of these, 20% prefer to receive it via SMS and 42% via email. Willingness to receive electronic messages about the UV index was associated with being unsure about whether a suntanned person would look healthy and greater use of sun protection in the past. Careful attention to message framing and timing of message delivery and focus on short-term effects of sun exposure such as sunburn and skin ageing should increase the acceptability of such messages to young people. We conclude that sun protection messages delivered to young adults via electronic media appear feasible and acceptable

The Integrated Calibration Index (ICI) and related metrics for quantifying the calibration of logistic regression models

Assessing the calibration of methods for estimating the probability of the occurrence of a binary outcome is an important aspec

Purposeful selection of variables in logistic regression

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Effect of Reimbursement Reductions on Bone Mineral Density Testing for Female Medicare Beneficiaries

Abstract Background: We examined whether the recent reimbursement reductions on the bone mineral density (BMD) test affected BMD testing in female Medicare beneficiaries with or without supplemental private health insurance. Methods: Retrospectively analyzing hospital administrative and clinical data on female Medicare beneficiaries (n=1320), we reviewed whether participants received BMD testing before (January 2004?December 2006) or after (January 2007?December 2009) reimbursement reductions for BMD testing. After adjusting for demographics and clinical characteristics, we performed Cox proportional hazard regression analyses of the BMD test including data from all study participants; we then performed separate regression analyses using data with or without supplemental private health insurance. Results: In those without supplemental private health insurance (n=421), less frequent BMD testing occurred after reimbursement reductions for BMD testing (hazard ratio [HR] 0.67, 95% confidence intervals [CI] 0.34-0.98; p=0.03). By contrast, in the overall participants (n=1320) and those with supplemental private health insurance (n=899), the number of BMD tests did not change significantly after reimbursement reductions for BMD testing. Conclusions: We found a significant association between reimbursement reductions and decrease in BMD tests in female Medicare beneficiaries without supplemental private health insurance.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98463/1/jwh%2E2012%2E3517.pd

Graphical and numerical diagnostic tools to assess suitability of multiple imputations and imputation models

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/122409/1/sim6926_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/122409/2/sim6926.pd

Improving SIEM for critical SCADA water infrastructures using machine learning

Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems. Supervisory control and data acquisition (SCADA) systems are used in industrial, infrastructure and facility processes (e.g. manufacturing, fabrication, oil and water pipelines, building ventilation, etc.) Like other Internet of Things (IoT) implementations, SCADA systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection is a major requirement. However, having an accurate anomaly detection system is not an easy task, due to the difficulty to differentiate between cyber-attacks and system internal failures (e.g. hardware failures). In this paper, we present a model that detects anomaly events in a water system controlled by SCADA. Six Machine Learning techniques have been used in building and evaluating the model. The model classifies different anomaly events including hardware failures (e.g. sensor failures), sabotage and cyber-attacks (e.g. DoS and Spoofing). Unlike other detection systems, our proposed work helps in accelerating the mitigation process by notifying the operator with additional information when an anomaly occurs. This additional information includes the probability and confidence level of event(s) occurring. The model is trained and tested using a real-world dataset

Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer

With the long-term rapid increase in incidences of colorectal cancer (CRC), there is an urgent clinical need to improve risk stratification. The conventional pathology report is usually limited to only a few histopathological features. However, most of the tumor microenvironments used to describe patterns of aggressive tumor behavior are ignored. In this work, we aim to learn histopathological patterns within cancerous tissue regions that can be used to improve prognostic stratification for colorectal cancer. To do so, we propose a self-supervised learning method that jointly learns a representation of tissue regions as well as a metric of the clustering to obtain their underlying patterns. These histopathological patterns are then used to represent the interaction between complex tissues and predict clinical outcomes directly. We furthermore show that the proposed approach can benefit from linear predictors to avoid overfitting in patient outcomes predictions. To this end, we introduce a new well-characterized clinicopathological dataset, including a retrospective collective of 374 patients, with their survival time and treatment information. Histomorphological clusters obtained by our method are evaluated by training survival models. The experimental results demonstrate statistically significant patient stratification, and our approach outperformed the state-of-the-art deep clustering methods

Cumulative Lead Exposure and Tooth Loss in Men: The Normative Aging Study

Background: Individuals previously exposed to lead remain at risk because of endogenous release of lead stored in their skeletal compartments. However, it is not known if long-term cumulative lead exposure is a risk factor for tooth loss. Objectives: We examined the association of bone lead concentrations with loss of natural teeth. Methods: We examined 333 men enrolled in the Veterans Affairs Normative Aging Study. We used a validated K-shell X-ray fluorescence (KXRF) method to measure lead concentrations in the tibial midshaft and patella. A dentist recorded the number of teeth remaining, and tooth loss was categorized as 0, 1–8 or ≥ 9 missing teeth. We used proportional odds models to estimate the association of bone lead biomarkers with tooth loss, adjusting for age, smoking, diabetes, and other putative confounders. Results: Participants with ≥ 9 missing teeth had significantly higher bone lead concentrations than those who had not experienced tooth loss. In multivariable-adjusted analyses, men in the highest tertile of tibia lead (> 23 μg/g) and patella lead (> 36 μg/g) had approximately three times the odds of having experienced an elevated degree of tooth loss (≥ 9 vs. 0–8 missing teeth or ≥ 1 vs. 0 missing teeth) as those in the lowest tertile [prevalence odds ratio (OR) = 3.03; 95% confidence interval (CI), 1.60–5.76 and OR = 2.41; 95% CI, 1.30–4.49, respectively]. Associations between bone lead biomarkers and tooth loss were similar in magnitude to the increased odds observed in participants who were current smokers. Conclusion: Long-term cumulative lead exposure is associated with increased odds of tooth loss

Machine learning based IoT Intrusion Detection System:an MQTT case study (MQTT-IoT-IDS2020 Dataset)

The Internet of Things (IoT) is one of the main research fields in the Cybersecurity domain. This is due to (a) the increased dependency on automated device, and (b) the inadequacy of general-purpose Intrusion Detection Systems (IDS) to be deployed for special purpose networks usage. Numerous lightweight protocols are being proposed for IoT devices communication usage. One of the distinguishable IoT machine-to-machine communication protocols is Message Queuing Telemetry Transport (MQTT) protocol. However, as per the authors best knowledge, there are no available IDS datasets that include MQTT benign or attack instances and thus, no IDS experimental results available. In this paper, the effectiveness of six Machine Learning (ML) techniques to detect MQTT-based attacks is evaluated. Three abstraction levels of features are assessed, namely, packet-based, unidirectional flow, and bidirectional flow features. An MQTT simulated dataset is generated and used for the training and evaluation processes. The dataset is released with an open access licence to help the research community further analyse the accompanied challenges. The experimental results demonstrated the adequacy of the proposed ML models to suit MQTT-based networks IDS requirements. Moreover, the results emphasise on the importance of using flow-based features to discriminate MQTT-based attacks from benign traffic, while packet-based features are sufficient for traditional networking attacks

Investigating the missing data mechanism in quality of life outcomes: a comparison of approaches

Background: Missing data is classified as missing completely at random (MCAR), missing at random (MAR) or missing not at random (MNAR). Knowing the mechanism is useful in identifying the most appropriate analysis. The first aim was to compare different methods for identifying this missing data mechanism to determine if they gave consistent conclusions. Secondly, to investigate whether the reminder-response data can be utilised to help identify the missing data mechanism. Methods: Five clinical trial datasets that employed a reminder system at follow-up were used. Some quality of life questionnaires were initially missing, but later recovered through reminders. Four methods of determining the missing data mechanism were applied. Two response data scenarios were considered. Firstly, immediate data only; secondly, all observed responses (including reminder-response). Results: In three of five trials the hypothesis tests found evidence against the MCAR assumption. Logistic regression suggested MAR, but was able to use the reminder-collected data to highlight potential MNAR data in two trials. Conclusion: The four methods were consistent in determining the missingness mechanism. One hypothesis test was preferred as it is applicable with intermittent missingness. Some inconsistencies between the two data scenarios were found. Ignoring the reminder data could potentially give a distorted view of the missingness mechanism. Utilising reminder data allowed the possibility of MNAR to be considered.The Chief Scientist Office of the Scottish Government Health Directorate. Research Training Fellowship (CZF/1/31